hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914177075904512 |
|---|---|
| author | Schulte, Jan-Frederik Ramhorst, Benjamin Sun, Chang Mitrevski, Jovan Ghielmetti, Nicolò Lupi, Enrico Danopoulos, Dimitrios Loncar, Vladimir Duarte, Javier Burnette, David Laatu, Lauri Tzelepis, Stylianos Axiotis, Konstantinos Berthet, Quentin Wang, Haoyan White, Paul Demirsoy, Suleyman Colombo, Marco Aarrestad, Thea Summers, Sioni Pierini, Maurizio Di Guglielmo, Giuseppe Ngadiuba, Jennifer Campos, Javier Hawks, Ben Gandrakota, Abhijith Fahim, Farah Tran, Nhan Constantinides, George Que, Zhiqiang Luk, Wayne Tapper, Alexander Hoang, Duc Paladino, Noah Harris, Philip Lai, Bo-Cheng Valentin, Manuel Forelli, Ryan Ogrenci, Seda Gerlach, Lino Flynn, Rian Liu, Mia Diaz, Daniel Khoda, Elham Quinnan, Melissa Solares, Russell Parajuli, Santosh Neubauer, Mark Herwig, Christian Tsoi, Ho Fung Rankin, Dylan Hsu, Shih-Chieh Hauck, Scott |
| author_facet | Schulte, Jan-Frederik Ramhorst, Benjamin Sun, Chang Mitrevski, Jovan Ghielmetti, Nicolò Lupi, Enrico Danopoulos, Dimitrios Loncar, Vladimir Duarte, Javier Burnette, David Laatu, Lauri Tzelepis, Stylianos Axiotis, Konstantinos Berthet, Quentin Wang, Haoyan White, Paul Demirsoy, Suleyman Colombo, Marco Aarrestad, Thea Summers, Sioni Pierini, Maurizio Di Guglielmo, Giuseppe Ngadiuba, Jennifer Campos, Javier Hawks, Ben Gandrakota, Abhijith Fahim, Farah Tran, Nhan Constantinides, George Que, Zhiqiang Luk, Wayne Tapper, Alexander Hoang, Duc Paladino, Noah Harris, Philip Lai, Bo-Cheng Valentin, Manuel Forelli, Ryan Ogrenci, Seda Gerlach, Lino Flynn, Rian Liu, Mia Diaz, Daniel Khoda, Elham Quinnan, Melissa Solares, Russell Parajuli, Santosh Neubauer, Mark Herwig, Christian Tsoi, Ho Fung Rankin, Dylan Hsu, Shih-Chieh Hauck, Scott |
| contents | We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware Schulte, Jan-Frederik Ramhorst, Benjamin Sun, Chang Mitrevski, Jovan Ghielmetti, Nicolò Lupi, Enrico Danopoulos, Dimitrios Loncar, Vladimir Duarte, Javier Burnette, David Laatu, Lauri Tzelepis, Stylianos Axiotis, Konstantinos Berthet, Quentin Wang, Haoyan White, Paul Demirsoy, Suleyman Colombo, Marco Aarrestad, Thea Summers, Sioni Pierini, Maurizio Di Guglielmo, Giuseppe Ngadiuba, Jennifer Campos, Javier Hawks, Ben Gandrakota, Abhijith Fahim, Farah Tran, Nhan Constantinides, George Que, Zhiqiang Luk, Wayne Tapper, Alexander Hoang, Duc Paladino, Noah Harris, Philip Lai, Bo-Cheng Valentin, Manuel Forelli, Ryan Ogrenci, Seda Gerlach, Lino Flynn, Rian Liu, Mia Diaz, Daniel Khoda, Elham Quinnan, Melissa Solares, Russell Parajuli, Santosh Neubauer, Mark Herwig, Christian Tsoi, Ho Fung Rankin, Dylan Hsu, Shih-Chieh Hauck, Scott Hardware Architecture Machine Learning High Energy Physics - Experiment We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results. |
| title | hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware |
| topic | Hardware Architecture Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2512.01463 |